CN105242137A - Line fault identification method using polar fault current principle component cluster analysis - Google Patents
Line fault identification method using polar fault current principle component cluster analysis Download PDFInfo
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- 238000012360 testing method Methods 0.000 claims abstract description 20
- 230000005540 biological transmission Effects 0.000 claims abstract description 19
- 238000005070 sampling Methods 0.000 claims abstract description 8
- 238000004088 simulation Methods 0.000 claims abstract description 5
- 230000001052 transient effect Effects 0.000 claims abstract description 5
- 238000000513 principal component analysis Methods 0.000 claims description 35
- 239000000470 constituent Substances 0.000 claims description 9
- 239000011888 foil Substances 0.000 claims description 2
- 238000005259 measurement Methods 0.000 abstract 1
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Abstract
The invention relates to a line fault identification method using polar fault current principle component cluster analysis, and belongs to the technical field of DC power transmission line protection. A batch of line metallicity grounding faults and line external faults are preset from near to far within a full-line-length scope, the sampling rate is 10kHz, a line fault curve of a fault current curve cluster of a measurement end is obtained through electromagnetic transient simulation, after the fault current curve cluster is subjected to normalization processing with a mean value of 0 and a variance of 1, principle component analysis (PCA) is carried out, a PCA space is formed, two clustering point clusters reflecting the line faults and the external faults are formed in the PCA space, a projection ot (q1, q2) of test sample data on a PC1-PC2 coordinate axis of the PCA cluster space is calculated, and distances between the test sample data and the cluster centers of the current curve clusters are measured by use of Euclidean distance so that the line faults are identified.
Description
Technical field
The present invention relates to a kind of line fault recognition methods utilizing polar curve fault current classified analysis on major constituents, belong to protection of direct current supply line technical field.
Background technology
Along with the broad development of straight-flow system application, at present the research of DC line protection is often paid close attention to and the Protection criteria of existing practical application is improved, and often adopt single definite value to carry out protection seting.It is line fault that DC transmission system fault is about half.The traveling-wave protection being core criterion with voltage change ratio du/dt is difficult to rely on single setting valve reliable in time broadening, effectively to detect and screens out fault possible in the scope of all fronts, accomplishes complete fibre.At present the research of DC line protection is often paid close attention to and the Protection criteria of existing practical application is improved, and often adopt single definite value to carry out protection seting.Because UHVDC circuit fed distance is usually far away; line fault reason is very complicated; cause insulator arc-over, common short circuit, bird pest, icing just like thunderbolt circuit, deice spring, mountain fire fault and the circuit nonlinear time-varying high resistive fault to tree electric discharge formation; often be difficult to characterize and resolve these faults by explicit mathematical relation, therefore only rely on adjustment protection definite value to be difficult to reliably realize complete fibre.Operation shows, line fault also exists repeatability, even often there is the phenomenon of the fault that the close same position of circuit often sends out reason similar.
Principal component analysis (PCA) (PCA) is a kind of analytical approach, its object is to set up a kind of classifying method, by a collection of sample data, analyze in " density degree in feature " by it, the similarity of the sample data within same class is arrived maximum, and the otherness between inhomogeneity reach maximum.On mathematical principle, PCA principal component analysis (PCA) is by the translation of data coordinates and rotation, makes there is higher similarity between any two sample datas of bunch class inside, and has higher diversity factor between two sample datas belonging to different bunch class.As can be seen here, PCA cluster analysis can characterize, portrays and distinguish DC line inside and outside portion fault, realizes the classification between line fault and external area error mode and identification.
Summary of the invention
The object of this invention is to provide a kind of line fault recognition methods utilizing polar curve fault current classified analysis on major constituents, in order to solve the problem.
Technical scheme of the present invention is: a kind of line fault recognition methods utilizing polar curve fault current classified analysis on major constituents, a collection of wired foil earth fault and circuit external fault is preset by near to far away in the long scope in all fronts, sampling rate is 10kHz, the line fault curve of the fault current curve family of measuring end is obtained by electromagnetic transient simulation, and by fault current curve family through average be zero, variance is after the normalized of 1, carry out PCA principal component analysis (PCA), and form PCA space, 2 class cluster points bunch of reflection line fault and external fault are spatially formed at this PCA, calculate test sample book data at PCA Cluster space PC
1, PC
2projection o in coordinate axis
t(q
1, q
2), adopt Euclidean distance to measure the distance of test sample book data and current curve bunch cluster centre, thus identification circuit fault.
It is characterized in that the concrete steps of monitoring recognition methods are as follows:
(1) under 10kHz sampling rate, when positive pole circuit generation earth fault, in completely long scope every 5km by near to far arranging metallic earthing fault, and rectification side fault and inverter side fault outside setting area.The line fault curve of the fault current curve family of measuring end is obtained by electromagnetic transient simulation.
(2) by electric current when emulating the fault current curve family that obtains normally to run for after benchmark is normalized, choose fault initial row ripple in sample data and arrive front 3 sampled points of measuring end, fault traveling wave arrives the data of 7 sampled points after measuring end and carries out PCA principal component analysis (PCA), and emulation obtains current curve bunch at PCA cluster result spatially.
(3) according to the PCA Cluster space actual conditions that step 2 obtains, direct current transmission line fault cluster is designed and chooses k center (k >=1); P center (generally get p=1, also rectification side fault and inverter side fault respectively can be established 1 cluster centre) is chosen to DC power transmission line external fault;
(4) break down, after protection element starts, when getting 1ms, in window, polar curve fault current data, as test sample book, calculate the PCA Cluster space PC that test sample book data obtain in step 1
1, PC
2projection o in coordinate axis
t(q
1, q
2);
(5) projection o is calculated
t(q
1, q
2) with the Euclidean distance of each cluster centre.Calculate the Euclidean distance at k center of test sample book data and line fault cluster, and be designated as d respectively
1, d
2..., d
k; In like manner, calculate the Euclidean distance at p center of test sample book data and circuit external fault cluster, and be designated as d respectively
k+1..., d
k+p.
(6) according to step 5, d is compared
1, d
2..., d
k..., d
k+psize, finds minimum value d
min.If d
min=d
1(or d
2..., d
k), be then judged as direct current transmission line fault mode; If d
min=d
k+1(or d
k+2..., d
k+p), be then judged as DC power transmission line external fault mode.
The invention has the beneficial effects as follows:
(1) this method be it without the need to adjusting, the acting characteristic of existing traveling-wave protection can be improved.
(2) as herein describedly select pole method, only use one-terminal data, principle is simple, does not rely on passage, invests little, forms relatively simple, protects more reliable.
Accompanying drawing explanation
Fig. 1 be positive pole circuit measuring end current curve bunch and at PCA cluster result spatially.
Fig. 2 be embodiment embodiment 1 medium cloud wide ± 800kV DC transmission system structural drawing.
Embodiment
Embodiment 1: cloud is wide ± 800kV DC transmission system realistic model structure as shown in Figure 2.Its line parameter circuit value is as follows: DC power transmission line total length is 1500km, and rectification side ground electrode circuit total length is 109km, and inverter side ground electrode circuit total length is 112km.The AC reactive compensation capacity of rectification side and inverter side is respectively 3000 and 3040Mvar, and arrange positive pole Location and be positioned at circuit distance M end 120km, transition resistance 50 Ω, data sampling rate is 10kHz.
(1) polar curve fault current classified analysis on major constituents space is built according to the step one in instructions to step 3;
(2) according to the step 4 in instructions, when getting 1ms, in window, polar curve fault current data, as test sample book, calculate the PCA Cluster space PC that test sample book data obtain in step 1
1, PC
2projection o in coordinate axis
t(q
1, q
2);
(3) d is obtained according to the step 5 in instructions
1=0.5243, d
2=5.9792.
(4) according to the step 6 in instructions, d is compared
1, d
2size, obtain d
min=d
1, be judged as direct current transmission line fault.
Embodiment 2: cloud is wide ± 800kV DC transmission system realistic model structure as shown in Figure 2.Its line parameter circuit value is as follows: DC power transmission line total length is 1500km, and rectification side ground electrode circuit total length is 109km, and inverter side ground electrode circuit total length is 112km.The AC reactive compensation capacity of rectification side and inverter side is respectively 3000 and 3040Mvar, and arrange positive pole Location and be positioned at circuit distance M end 950km, transition resistance 50 Ω, data sampling rate is 10kHz.
(1) polar curve fault current classified analysis on major constituents space is built according to the step one in instructions to step 3;
(2) according to the step 4 in instructions, when getting 1ms, in window, polar curve fault current data, as test sample book, calculate the PCA Cluster space PC that test sample book data obtain in step 1
1, PC
2projection o in coordinate axis
t(q
1, q
2);
(3) d is obtained according to the step 5 in instructions
1=2.1682, d
2=3.3410.
(4) according to the step 6 in instructions, d is compared
1, d
2size, obtain d
min=d
1, be judged as direct current transmission line fault.
Embodiment 3: cloud is wide ± 800kV DC transmission system realistic model structure as shown in Figure 2.Its line parameter circuit value is as follows: DC power transmission line total length is 1500km, and rectification side ground electrode circuit total length is 109km, and inverter side ground electrode circuit total length is 112km.The AC reactive compensation capacity of rectification side and inverter side is respectively 3000 and 3040Mvar, and arrange positive pole circuit rectification side outlet fault, transition resistance 10 Ω, data sampling rate is 10kHz.
(1) polar curve fault current classified analysis on major constituents space is built according to the step one in instructions to step 3;
(2) according to the step 4 in instructions, when getting 1ms, in window, polar curve fault current data, as test sample book, calculate the PCA Cluster space PC that test sample book data obtain in step 1
1, PC
2projection o in coordinate axis
t(q
1, q
2);
(3) d is obtained according to the step 5 in instructions
1=5.8861, d
2=0.3818.
(4) according to the step 6 in instructions, d is compared
1, d
2size, obtain d
min=d
2, be judged as circuit external fault.
Claims (2)
1. one kind utilizes the line fault recognition methods of polar curve fault current classified analysis on major constituents, it is characterized in that: in the long scope in all fronts, preset a collection of wired foil earth fault and circuit external fault by near to far away, sampling rate is 10kHz, the line fault curve of the fault current curve family of measuring end is obtained by electromagnetic transient simulation, and by fault current curve family through average be zero, variance is after the normalized of 1, carry out PCA principal component analysis (PCA), and form PCA space, 2 class cluster points bunch of reflection line fault and external fault are spatially formed at this PCA, calculate test sample book data at PCA Cluster space PC
1, PC
2projection o in coordinate axis
t(q
1, q
2), adopt Euclidean distance to measure the distance of test sample book data and current curve bunch cluster centre, thus identification circuit fault.
2. the line fault recognition methods utilizing polar curve fault current classified analysis on major constituents according to claim 1, is characterized in that concrete steps are as follows:
(1) under 10kHz sampling rate, when positive pole circuit generation earth fault, in the long scope in all fronts, extremely far metallic earthing fault is set every 5km by near, and rectification side fault and inverter side fault outside setting area, the line fault curve of the fault current curve family of measuring end is obtained by electromagnetic transient simulation;
(2) by electric current when emulating the fault current curve family that obtains normally to run for after benchmark is normalized, choose fault initial row ripple in sample data and arrive front 3 sampled points of measuring end, fault traveling wave arrives the data of 7 sampled points after measuring end and carries out PCA principal component analysis (PCA), and emulation obtains current curve bunch at PCA cluster result spatially;
(3) according to the PCA Cluster space actual conditions that step 2 obtains, direct current transmission line fault cluster is designed and chooses k center (k >=1); P center is chosen to DC power transmission line external fault;
(4) break down, after protection element starts, when getting 1ms, in window, polar curve fault current data, as test sample book, calculate the PCA Cluster space PC that test sample book data obtain in step 1
1, PC
2projection o in coordinate axis
t(q
1, q
2);
(5) projection o is calculated
t(q
1, q
2) with the Euclidean distance of each cluster centre, calculate the Euclidean distance at k center of test sample book data and line fault cluster, and be designated as d respectively
1, d
2..., d
k; In like manner, calculate the Euclidean distance at p center of test sample book data and circuit external fault cluster, and be designated as d respectively
k+1..., d
k+p;
(6) according to step 5, d is compared
1, d
2..., d
k..., d
k+psize, finds minimum value d
min;
If d
min=d
1(or d
2..., d
k), be then judged as direct current transmission line fault mode;
If d
min=d
k+1(or d
k+2..., d
k+p), be then judged as DC power transmission line external fault mode.
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Cited By (5)
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| CN106291239A (en) * | 2016-08-02 | 2017-01-04 | 昆明理工大学 | A kind of direct current transmission line fault recognition methods using filter branches electric current and principal component analytical method |
| CN106353638A (en) * | 2016-08-31 | 2017-01-25 | 广西电网有限责任公司电力科学研究院 | Fault line selection method based on transient current projection component projection coefficient comparison |
| CN107525993A (en) * | 2017-06-30 | 2017-12-29 | 昆明理工大学 | A kind of list based on hierarchical clustering algorithm fault distinguishing method forever |
| CN110703036A (en) * | 2019-10-09 | 2020-01-17 | 江苏方天电力技术有限公司 | Clustering-based high-resistance grounding fault positioning method for resonant grounding system |
| CN112801135A (en) * | 2020-12-31 | 2021-05-14 | 浙江浙能镇海发电有限责任公司 | Fault line selection method and device for power plant service power system based on characteristic quantity correlation |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106291239A (en) * | 2016-08-02 | 2017-01-04 | 昆明理工大学 | A kind of direct current transmission line fault recognition methods using filter branches electric current and principal component analytical method |
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| CN107525993A (en) * | 2017-06-30 | 2017-12-29 | 昆明理工大学 | A kind of list based on hierarchical clustering algorithm fault distinguishing method forever |
| CN110703036A (en) * | 2019-10-09 | 2020-01-17 | 江苏方天电力技术有限公司 | Clustering-based high-resistance grounding fault positioning method for resonant grounding system |
| CN110703036B (en) * | 2019-10-09 | 2021-09-14 | 江苏方天电力技术有限公司 | Clustering-based high-resistance grounding fault positioning method for resonant grounding system |
| CN112801135A (en) * | 2020-12-31 | 2021-05-14 | 浙江浙能镇海发电有限责任公司 | Fault line selection method and device for power plant service power system based on characteristic quantity correlation |
| CN112801135B (en) * | 2020-12-31 | 2023-04-18 | 浙江浙能镇海发电有限责任公司 | Fault line selection method and device for power plant service power system based on characteristic quantity correlation |
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